Python cv2.ORB Examples
The following are 9
code examples of cv2.ORB().
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Example #1
| Source File: findobj.py From airtest with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv2.SIFT()
norm = cv2.NORM_L2
elif chunks[0] == 'surf':
detector = cv2.SURF(800)
norm = cv2.NORM_L2
elif chunks[0] == 'orb':
detector = cv2.ORB(400)
norm = cv2.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
else:
matcher = cv2.BFMatcher(norm)
return detector, matcher
Example #2
| Source File: find_obj.py From airtest with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv2.SIFT()
norm = cv2.NORM_L2
elif chunks[0] == 'surf':
detector = cv2.SURF(800)
norm = cv2.NORM_L2
elif chunks[0] == 'orb':
detector = cv2.ORB(400)
norm = cv2.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
else:
matcher = cv2.BFMatcher(norm)
return detector, matcher
Example #3
| Source File: find_obj.py From ImageAnalysis with MIT License | 6 votes |
|
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv2.SIFT()
norm = cv2.NORM_L2
elif chunks[0] == 'surf':
detector = cv2.SURF(400)
norm = cv2.NORM_L2
elif chunks[0] == 'orb':
detector = cv2.ORB(400)
norm = cv2.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
else:
matcher = cv2.BFMatcher(norm)
return detector, matcher
Example #4
| Source File: find_obj.py From PyCV-time with MIT License | 6 votes |
|
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv2.SIFT()
norm = cv2.NORM_L2
elif chunks[0] == 'surf':
detector = cv2.SURF(800)
norm = cv2.NORM_L2
elif chunks[0] == 'orb':
detector = cv2.ORB(400)
norm = cv2.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
else:
matcher = cv2.BFMatcher(norm)
return detector, matcher
Example #5
| Source File: ftlib.py From sea_ice_drift with GNU General Public License v3.0 | 5 votes |
|
def find_key_points(image,
edgeThreshold=34,
nFeatures=100000,
nLevels=7,
patchSize=34,
**kwargs):
''' Initiate detector and find key points on an image
Parameters
----------
image : 2D UInt8 Numpy array - image
edgeThreshold : int - parameter for OpenCV detector
nFeatures : int - parameter for OpenCV detector
nLevels : int - parameter for OpenCV detector
patchSize : int - parameter for OpenCV detector
Returns
-------
keyPoints : list - coordinates of keypoint on image
descriptors : list - binary descriptos of kepoints
'''
if cv2.__version__.startswith('3.') or cv2.__version__.startswith('4.'):
detector = cv2.ORB_create()
detector.setEdgeThreshold(edgeThreshold)
detector.setMaxFeatures(nFeatures)
detector.setNLevels(nLevels)
detector.setPatchSize(patchSize)
else:
detector = cv2.ORB()
detector.setInt('edgeThreshold', edgeThreshold)
detector.setInt('nFeatures', nFeatures)
detector.setInt('nLevels', nLevels)
detector.setInt('patchSize', patchSize)
print('ORB detector initiated')
keyPoints, descriptors = detector.detectAndCompute(image, None)
print('Key points found: %d' % len(keyPoints))
return keyPoints, descriptors
Example #6
| Source File: plane_tracker.py From PyCV-time with MIT License | 5 votes |
|
def __init__(self):
self.detector = cv2.ORB( nfeatures = 1000 )
self.matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
self.targets = []
Example #7
| Source File: plane_tracker.py From PyCV-time with MIT License | 5 votes |
|
def __init__(self):
self.detector = cv2.ORB( nfeatures = 1000 )
self.matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
self.targets = []
Example #8
| Source File: find_obj.py From PyCV-time with MIT License | 5 votes |
|
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv2.xfeatures2d.SIFT()
norm = cv2.NORM_L2
elif chunks[0] == 'surf':
detector = cv2.xfeatures2d.SURF(800)
norm = cv2.NORM_L2
elif chunks[0] == 'orb':
detector = cv2.ORB(400)
norm = cv2.NORM_HAMMING
elif chunks[0] == 'akaze':
detector = cv2.AKAZE()
norm = cv2.NORM_HAMMING
elif chunks[0] == 'brisk':
detector = cv2.BRISK()
norm = cv2.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
else:
matcher = cv2.BFMatcher(norm)
return detector, matcher
Example #9
| Source File: servoing_designed_features_quad_panda3d_env.py From citysim3d with MIT License | 4 votes |
|
def __init__(self, action_space, feature_type=None, filter_features=None,
max_time_steps=100, distance_threshold=4.0, **kwargs):
"""
filter_features indicates whether to filter out key points that are not
on the object in the current image. Key points in the target image are
always filtered out.
"""
SimpleQuadPanda3dEnv.__init__(self, action_space, **kwargs)
ServoingEnv.__init__(self, env=self, max_time_steps=max_time_steps, distance_threshold=distance_threshold)
lens = self.camera_node.node().getLens()
self._observation_space.spaces['points'] = BoxSpace(np.array([-np.inf, lens.getNear(), -np.inf]),
np.array([np.inf, lens.getFar(), np.inf]))
film_size = tuple(int(s) for s in lens.getFilmSize())
self.mask_camera_sensor = Panda3dMaskCameraSensor(self.app, (self.skybox_node, self.city_node),
size=film_size,
near_far=(lens.getNear(), lens.getFar()),
hfov=lens.getFov())
for cam in self.mask_camera_sensor.cam:
cam.reparentTo(self.camera_sensor.cam)
self.filter_features = True if filter_features is None else False
self._feature_type = feature_type or 'sift'
if cv2.__version__.split('.')[0] == '3':
from cv2.xfeatures2d import SIFT_create, SURF_create
from cv2 import ORB_create
if self.feature_type == 'orb':
# https://github.com/opencv/opencv/issues/6081
cv2.ocl.setUseOpenCL(False)
else:
SIFT_create = cv2.SIFT
SURF_create = cv2.SURF
ORB_create = cv2.ORB
if self.feature_type == 'sift':
self._feature_extractor = SIFT_create()
elif self.feature_type == 'surf':
self._feature_extractor = SURF_create()
elif self.feature_type == 'orb':
self._feature_extractor = ORB_create()
else:
raise ValueError("Unknown feature extractor %s" % self.feature_type)
if self.feature_type == 'orb':
self._matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
else:
self._matcher = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
self._target_key_points = None
self._target_descriptors = None
